library(tidyverse)

── Attaching packages ───────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.4.0  
✔ readr   1.3.1       ✔ forcats 0.4.0  
── Conflicts ──────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(ggvoronoi)
library(viridis)

Loading required package: viridisLite

library(ggthemes)
library(cluster)
library(igraph)

Attaching package: ‘igraph’

The following objects are masked from ‘package:dplyr’:

    as_data_frame, groups, union

The following objects are masked from ‘package:purrr’:

    compose, simplify

The following object is masked from ‘package:tidyr’:

    crossing

The following object is masked from ‘package:tibble’:

    as_data_frame

The following objects are masked from ‘package:stats’:

    decompose, spectrum

The following object is masked from ‘package:base’:

    union

library(readxl)

set.seed(1234)

similitud <- read_csv('results/similitud_LP_2016.csv',col_types = cols(SITC = col_character()))
clasificacion <-read_xlsx("../LDA/names/classifications_hidalgo.xlsx", sheet = 'sitc_product_id') %>% 
  rename(medioide=sitc_product_code)

M <- as.matrix(similitud[,-1])
#distance matrix
DM <- 1/M

plot_pam_giant_graph <- function(M,pam_clust,threshold = 0.5){
  
#grafo
adj_mat <- M
#pongo un threshold
adj_mat[adj_mat<threshold] <- 0

g  <- graph_from_adjacency_matrix(adj_mat, weighted=TRUE,mode='undirected')

graphs <- decompose.graph(g)
comp_gigante <- graphs[[which.max(sapply(graphs, vcount))]]

# Pongo solo la etiqueta de los medioides

names <- V(comp_gigante)$name
mediod_label <- names
ind <- which(!mediod_label %in% pam_clust$medoids) 
mediod_label[ind]<-NA
V(comp_gigante)$label <- mediod_label

colors <- tibble(cluster = unique(pam_clust$clustering), color = colorspace::rainbow_hcl(length(unique(pam_clust$clustering)),c = 100, l = 60, start = 0,alpha = 0.75))

colors_df <- tibble(names) %>% 
  left_join(.,tibble(names = names(pam_clust$clustering),cluster = pam_clust$clustering)) %>% 
  left_join(colors)

V(comp_gigante)$color <- colors_df$color

l <-layout_nicely(comp_gigante)

plot(comp_gigante,edge.arrow.size=.4,vertex.frame.color="#ffffff", edge.size = .1,
     vertex.label=V(comp_gigante)$label, vertex.label.color="black",vertex.size = 5,
     vertex.label.cex=1.2,  layout=l)

}

plot_pam_max_span <- function(M,pam_clust,threshold = 0.5){
  
#grafo
adj_mat <- M
#pongo un threshold
# adj_mat[adj_mat<threshold] <- 0

g  <- graph_from_adjacency_matrix(adj_mat, weighted=TRUE)

g_mst <- mst(g)  
# Pongo solo la etiqueta de los medioides

names <- V(g_mst)$name
mediod_label <- names
ind <- which(!mediod_label %in% pam_clust$medoids) 
mediod_label[ind]<-NA
V(g_mst)$label <- mediod_label

colors <- tibble(cluster = unique(pam_clust$clustering), color = colorspace::rainbow_hcl(length(unique(pam_clust$clustering)),c = 100, l = 60, start = 0,alpha = 0.75))

colors_df <- tibble(names) %>% 
  left_join(.,tibble(names = names(pam_clust$clustering),cluster = pam_clust$clustering)) %>% 
  left_join(colors)

V(g_mst)$color <- colors_df$color

g_mst <-  decompose.graph(g_mst)[[1]]

# l <-layout_nicely(g_mst)

plot(g_mst,edge.arrow.size=.4,vertex.frame.color="#ffffff", edge.size = .1,
     vertex.label=V(g_mst)$label, vertex.label.color="black",vertex.size = 5,
     vertex.label.cex=1.2)#,  layout=l)

}

plot_pam_giant_graph(M,pam_clust2)

Joining, by = "names"
Joining, by = "cluster"

pam_clust10 <- pam(DM,diss = TRUE,k=10)

png('results/pam10_gigant_lp.png',width = 6,height = 6, units = 'in', res = 300)
plot_pam_giant_graph(M,pam_clust10)

Joining, by = "names"
Joining, by = "cluster"

dev.off()

null device 
          1 

png('results/pam10_mst_lp.png',width = 6,height = 6, units = 'in', res = 300)

plot_pam_max_span(M,pam_clust10)

Joining, by = "names"
Joining, by = "cluster"

dev.off()

null device 
          1 

pam_clust50 <- pam(DM,diss = TRUE,k=50)

png('results/pam50_gigant_lp.png',width = 6,height = 6, units = 'in', res = 300)
plot_pam_giant_graph(M,pam_clust50)

Joining, by = "names"
Joining, by = "cluster"

dev.off()

null device 
          1 

png('results/pam50_mst_lp.png',width = 6,height = 6, units = 'in', res = 300)

plot_pam_max_span(M,pam_clust50)

Joining, by = "names"
Joining, by = "cluster"

dev.off()

null device 
          1 

tibble(k = '2', medioide= pam_clust2$medoids) %>% 
  # bind_rows(tibble(k = '10', medioide= pam_clust10$medoids)) %>% 
  # bind_rows(tibble(k = 50, medioide= pam_clust50$medoids)) %>% 
  left_join(clasificacion %>% select(-id)) %>% 
  xtable::xtable(., caption='Medioides', label='table:pam') %>% 
  xtable::print.xtable(.,include.rownames=FALSE)

Joining, by = "medioide"

% latex table generated in R 3.5.1 by xtable 1.8-3 package
% Sat Apr 13 17:40:10 2019
\begin{table}[ht]
\centering
\begin{tabular}{lll}
  \hline
k & medioide & sitc\_product\_name\_short\_en \\ 
  \hline
2 & 0589 & Fruit prepared or preserved, nes \\ 
  2 & 7413 & Industrial and laboratory furnaces and ovens, etc, parts, nes \\ 
   \hline
\end{tabular}
\caption{Medioides} 
\label{table:pam}
\end{table}

  tibble(k = '10', medioide= pam_clust10$medoids) %>%
  left_join(clasificacion %>% select(-id)) %>% 
  xtable::xtable(., caption='Medioides', label='table:pam') %>% 
  xtable::print.xtable(.,include.rownames=FALSE)

Joining, by = "medioide"

% latex table generated in R 3.5.1 by xtable 1.8-3 package
% Sat Apr 13 17:40:34 2019
\begin{table}[ht]
\centering
\begin{tabular}{lll}
  \hline
k & medioide & sitc\_product\_name\_short\_en \\ 
  \hline
10 & 6911 & Structures and parts of, of iron, steel; plates, rods, and the like \\ 
  10 & 0542 & Beans, peas, other leguminous vegetables, dried, shelled \\ 
  10 & 0116 & Edible offal of headings 0011-5 and 0015, fresh, chilled or frozen \\ 
  10 & 8452 & Outerwear knitted or crocheted, not elastic nor rubberized; womens, girls, infants, suits, dresses, etc, knitted, crocheted \\ 
  10 & 7429 & Parts, nes of pumps and liquids elevators falling in heading 742 \\ 
  10 & 0721 & Cocoa beans, raw, roasted \\ 
  10 & 6536 & Fabrics, woven, 85\% plus of discontinuous regenerated fibres \\ 
  10 & 5827 & Silicones \\ 
  10 & 2816 & Iron ore agglomerates \\ 
  10 & 7764 & Electronic microcircuits \\ 
   \hline
\end{tabular}
\caption{Medioides} 
\label{table:pam}
\end{table}

tibble(k = '50', medioide= pam_clust50$medoids) %>%
  left_join(clasificacion) %>% 
  xtable::xtable(., caption='Medioides', label='table:pam') %>% 
  xtable::print.xtable(.,include.rownames=FALSE)

Joining, by = "medioide"

% latex table generated in R 3.5.1 by xtable 1.8-3 package
% Sat Apr 13 17:40:36 2019
\begin{table}[ht]
\centering
\begin{tabular}{llrl}
  \hline
k & medioide & id & sitc\_product\_name\_short\_en \\ 
  \hline
50 & 0484 & 696.00 & Bakery products \\ 
  50 & 6115 & 995.00 & Sheep and lamb skin leather \\ 
  50 & 0113 & 658.00 & Pig meat fresh, chilled or frozen \\ 
  50 & 5542 & 949.00 & Organic surface-active agents, nes \\ 
  50 & 0116 & 661.00 & Edible offal of headings 0011-5 and 0015, fresh, chilled or frozen \\ 
  50 & 2686 & 813.00 & Waste of sheep's or lambs' wool, or of other animal hair, nes \\ 
  50 & 6635 & 1094.00 & Wool; expanding or insulating mineral materials, nes \\ 
  50 & 0565 & 706.00 & Vegetables, prepared or preserved, nes \\ 
  50 & 0344 & 677.00 & Fish fillets, frozen \\ 
  50 & 0412 & 683.00 & Other wheat and meslin, unmilled \\ 
  50 & 2221 & 762.00 & Groundnuts, green \\ 
  50 & 7132 & 1189.00 & Motor vehicles piston engines, headings: 722; 78; 74411 and 95101 \\ 
  50 & 0542 & 699.00 & Beans, peas, other leguminous vegetables, dried, shelled \\ 
  50 & 0571 & 707.00 & Oranges, mandarins, etc, fresh or dried \\ 
  50 & 0611 & 720.00 & Sugars, beet and cane, raw, solid \\ 
  50 & 6536 & 1051.00 & Fabrics, woven, 85\% plus of discontinuous regenerated fibres \\ 
  50 & 2483 & 785.00 & Wood, non-coniferous species, sawn, planed, tongued, grooved, etc \\ 
  50 & 4242 & 888.00 & Palm oil \\ 
  50 & 0742 & 733.00 & Mate \\ 
  50 & 8435 & 1358.00 & Womens, girls, infants outerwear, textile, not knitted or crocheted; blouses \\ 
  50 & 6744 & 1127.00 & Sheet, plates, rolled of thickness 4,75mm plus, of iron or steel \\ 
  50 & 2640 & 798.00 & Jute, other textile bast fibres, nes, raw, processed but not spun \\ 
  50 & 5827 & 964.00 & Silicones \\ 
  50 & 6594 & 1081.00 & Carpets, rugs, mats, of wool or fine animal hair \\ 
  50 & 6330 & 1012.00 & Cork manufactures \\ 
  50 & 2518 & 790.00 & Chemical wood pulp, sulphite \\ 
  50 & 6511 & 1036.00 & Silk yarn and spun from noil or waste; silkworm gut \\ 
  50 & 6516 & 1041.00 & Yarn containing less than 85\% of discontinuous synthetic fibres \\ 
  50 & 2655 & 802.00 & Manila hemp, raw or processed but not spun, its tow and waste \\ 
  50 & 7369 & 1234.00 & Parts, nes of and accessories for machine-tools of heading 736 \\ 
  50 & 5623 & 953.00 & Mineral or chemical fertilizer, potassic \\ 
  50 & 6911 & 1159.00 & Structures and parts of, of iron, steel; plates, rods, and the like \\ 
  50 & 3222 & 860.00 & Other coal, not agglomerated \\ 
  50 & 6672 & 1114.00 & Diamonds (non-industrial), not mounted or set \\ 
  50 & 2875 & 843.00 & Zinc ores and concentrates \\ 
  50 & 2816 & 836.00 & Iron ore agglomerates \\ 
  50 & 5241 & 928.00 & Radio-active chemical elements, isotopes etc \\ 
  50 & 5225 & 923.00 & Inorganic bases and metallic oxides, hydroxides and peroxides \\ 
  50 & 2881 & 847.00 & Ash and residues, nes \\ 
  50 & 8481 & 1373.00 & Articles of apparel, clothing accessories of leather \\ 
  50 & 3341 & 866.00 & Gasoline and other light oils \\ 
  50 & 5162 & 916.00 & Aldehyde, ketone and quinone-function compounds \\ 
  50 & 7269 & 1224.00 & Parts, nes of machines falling within headings 72631, 7264, 7267 \\ 
  50 & 7492 & 1262.00 & Cocks, valves and similar appliances, for pipes boiler shells, etc \\ 
  50 & 7757 & 1301.00 & Domestic electro-mechanical appliances; and parts thereof, nes \\ 
  50 & 7245 & 1213.00 & Weaving, knitting, etc, machines, machines for preparing yarns, etc \\ 
  50 & 7763 & 1305.00 & Diodes, transistors, photocells, etc \\ 
  50 & 7628 & 1280.00 & Other radio receivers \\ 
  50 & 7525 & 1272.00 & Peripheral units, including control and adapting units \\ 
  50 & 7144 & 1193.00 & Reaction engines \\ 
   \hline
\end{tabular}
\caption{Medioides} 
\label{table:pam}
\end{table}

m2 <- similitud
#m2[,-1][lower.tri(m2[,-1],diag = TRUE)] <- NA

sim_table <- m2 %>% 
  gather(SITC_par,value = similarity,2:ncol(.)) %>%
  filter(!is.na(similarity)) %>% 
  mutate(distance = 1/similarity)

sim_table %>% 
  arrange(-similarity) %>% 
  top_n(10, similarity) %>%
  left_join(clasificacion %>% select(SITC= medioide, description=sitc_product_name_short_en)) %>% 
  left_join(clasificacion %>% select(SITC_par= medioide, description_par=sitc_product_name_short_en)) %>% 
  mutate(description = paste0(substr(description,1,22),'...'),
         description_par = paste0(substr(description_par,1,22),'...')) %>% 
  xtable::xtable(., caption='Productos más similares', label='table:similarity') %>% 
  xtable::print.xtable(.,include.rownames=FALSE)

Joining, by = "SITC"
Joining, by = "SITC_par"

% latex table generated in R 3.5.1 by xtable 1.8-3 package
% Sat Apr 13 18:07:13 2019
\begin{table}[ht]
\centering
\begin{tabular}{llrll}
  \hline
SITC & SITC\_par & similarity & description & description\_par \\ 
  \hline
8423 & 8439 & 0.83 & Men's and boys' outerw... & Womens, girls, infants... \\ 
  8431 & 8434 & 0.83 & Womens, girls, infants... & Womens, girls, infants... \\ 
  8433 & 8434 & 0.82 & Womens, girls, infants... & Womens, girls, infants... \\ 
  8459 & 8462 & 0.82 & Outerwear knitted or c... & Under-garments, knitte... \\ 
  8434 & 8435 & 0.81 & Womens, girls, infants... & Womens, girls, infants... \\ 
  8433 & 8435 & 0.80 & Womens, girls, infants... & Womens, girls, infants... \\ 
  8421 & 8424 & 0.80 & Men's and boys' outerw... & Men's and boys' outerw... \\ 
  8451 & 8459 & 0.80 & Outerwear knitted or c... & Outerwear knitted or c... \\ 
  8421 & 8431 & 0.80 & Men's and boys' outerw... & Womens, girls, infants... \\ 
  8422 & 8424 & 0.79 & Men's and boys' outerw... & Men's and boys' outerw... \\ 
   \hline
\end{tabular}
\caption{Productos más similares} 
\label{table:similarity}
\end{table}

mean_sim_table %>% 
  arrange(-similarity) %>%
  top_n(25, similarity) %>% 
  filter(row_number() %% 2 == 0) %>% 
  left_join(clasificacion %>% select(SITC=medioide,SITC_desc=Description)) %>% 
  left_join(clasificacion %>% select(SITC_par=medioide,SITC_par_desc=Description)) %>% 
  select(similarity,SITC, SITC_desc,SITC_par,SITC_par_desc) %>% 
  mutate(SITC_desc = tolower(SITC_desc),
         SITC_par_desc = tolower(SITC_par_desc),
         SITC_desc = str_remove(SITC_desc,",x-knit"),
         SITC_par_desc = str_remove(SITC_par_desc,",x-knit"),
         SITC_desc = str_remove(SITC_desc,".xknit"),
         SITC_par_desc = str_remove(SITC_par_desc,".xknit"),
         SITC_desc = str_remove(SITC_desc,".knit"),
         SITC_par_desc = str_remove(SITC_par_desc,".knit")) %>% 
  xtable::xtable(., caption='Productos más similares', label='table:similarity') %>% 
  xtable::print.xtable(.,include.rownames=FALSE)

---
title: "Similitud analysis Serie larga"
output: html_notebook
---


```{r setup}
library(tidyverse)
library(ggvoronoi)
library(viridis)
library(ggthemes)
library(cluster)
library(igraph)
library(readxl)

set.seed(1234)
```

```{r}
similitud <- read_csv('results/similitud_LP_2016.csv',col_types = cols(SITC = col_character()))
clasificacion <-read_xlsx("../LDA/names/classifications_hidalgo.xlsx", sheet = 'sitc_product_id') %>% 
  rename(medioide=sitc_product_code)


```

```{r}
M <- as.matrix(similitud[,-1])
#distance matrix
DM <- 1/M

```


# K medioids

```{r}

plot_pam_giant_graph <- function(M,pam_clust,threshold = 0.5){
  
#grafo
adj_mat <- M
#pongo un threshold
adj_mat[adj_mat<threshold] <- 0

g  <- graph_from_adjacency_matrix(adj_mat, weighted=TRUE,mode='undirected')

graphs <- decompose.graph(g)
comp_gigante <- graphs[[which.max(sapply(graphs, vcount))]]

# Pongo solo la etiqueta de los medioides

names <- V(comp_gigante)$name
mediod_label <- names
ind <- which(!mediod_label %in% pam_clust$medoids) 
mediod_label[ind]<-NA
V(comp_gigante)$label <- mediod_label

colors <- tibble(cluster = unique(pam_clust$clustering), color = colorspace::rainbow_hcl(length(unique(pam_clust$clustering)),c = 100, l = 60, start = 0,alpha = 0.75))

colors_df <- tibble(names) %>% 
  left_join(.,tibble(names = names(pam_clust$clustering),cluster = pam_clust$clustering)) %>% 
  left_join(colors)

V(comp_gigante)$color <- colors_df$color

l <-layout_nicely(comp_gigante)

plot(comp_gigante,edge.arrow.size=.4,vertex.frame.color="#ffffff", edge.size = .1,
     vertex.label=V(comp_gigante)$label, vertex.label.color="black",vertex.size = 5,
     vertex.label.cex=1.2,  layout=l)

}

```

### Max spanning tree

```{r}
plot_pam_max_span <- function(M,pam_clust,threshold = 0.5){
  
#grafo
adj_mat <- M
#pongo un threshold
# adj_mat[adj_mat<threshold] <- 0

g  <- graph_from_adjacency_matrix(adj_mat, weighted=TRUE)

g_mst <- mst(g)  
# Pongo solo la etiqueta de los medioides

names <- V(g_mst)$name
mediod_label <- names
ind <- which(!mediod_label %in% pam_clust$medoids) 
mediod_label[ind]<-NA
V(g_mst)$label <- mediod_label

colors <- tibble(cluster = unique(pam_clust$clustering), color = colorspace::rainbow_hcl(length(unique(pam_clust$clustering)),c = 100, l = 60, start = 0,alpha = 0.75))

colors_df <- tibble(names) %>% 
  left_join(.,tibble(names = names(pam_clust$clustering),cluster = pam_clust$clustering)) %>% 
  left_join(colors)

V(g_mst)$color <- colors_df$color

g_mst <-  decompose.graph(g_mst)[[1]]

# l <-layout_nicely(g_mst)

plot(g_mst,edge.arrow.size=.4,vertex.frame.color="#ffffff", edge.size = .1,
     vertex.label=V(g_mst)$label, vertex.label.color="black",vertex.size = 5,
     vertex.label.cex=1.2)#,  layout=l)

}
```


### pam k=2

```{r}
pam_clust2 <- pam(DM,diss = TRUE,k=2)

png('results/pam2_gigant_lp.png',width = 6,height = 6, units = 'in', res = 300)
plot_pam_giant_graph(M,pam_clust2)
dev.off()

png('results/pam2_mst_lp.png',width = 6,height = 6, units = 'in', res = 300)
plot_pam_max_span(M,pam_clust2)
dev.off()
```

### pam k=10

```{r}
pam_clust10 <- pam(DM,diss = TRUE,k=10)

png('results/pam10_gigant_lp.png',width = 6,height = 6, units = 'in', res = 300)
plot_pam_giant_graph(M,pam_clust10)
dev.off()

png('results/pam10_mst_lp.png',width = 6,height = 6, units = 'in', res = 300)
plot_pam_max_span(M,pam_clust10)
dev.off()
```

### pam k=50

```{r}
pam_clust50 <- pam(DM,diss = TRUE,k=50)

png('results/pam50_gigant_lp.png',width = 6,height = 6, units = 'in', res = 300)
plot_pam_giant_graph(M,pam_clust50)
dev.off()

png('results/pam50_mst_lp.png',width = 6,height = 6, units = 'in', res = 300)
plot_pam_max_span(M,pam_clust50)
dev.off()
```


```{r}
tibble(k = '2', medioide= pam_clust2$medoids) %>% 
  # bind_rows(tibble(k = '10', medioide= pam_clust10$medoids)) %>% 
  # bind_rows(tibble(k = 50, medioide= pam_clust50$medoids)) %>% 
  left_join(clasificacion %>% select(-id)) %>% 
  xtable::xtable(., caption='Medioides', label='table:pam') %>% 
  xtable::print.xtable(.,include.rownames=FALSE)
```

```{r}
  tibble(k = '10', medioide= pam_clust10$medoids) %>%
  left_join(clasificacion %>% select(-id)) %>% 
  xtable::xtable(., caption='Medioides', label='table:pam') %>% 
  xtable::print.xtable(.,include.rownames=FALSE)
```

```{r}

tibble(k = '50', medioide= pam_clust50$medoids) %>%
  left_join(clasificacion) %>% 
  xtable::xtable(., caption='Medioides', label='table:pam') %>% 
  xtable::print.xtable(.,include.rownames=FALSE)
```


# heatmap

```{r}

dim(dist(M))
hc <- hclust(dist(M))
plot(hc)

clust_col <- function(M) {
  M[M<0.5] <- 0
  DM_thr <- 1/M
  DM_thr[is.infinite(DM_thr)] <- 999
  d <- dist(DM_thr)
  hclust(d,method = "ward.D")
}


heatmap(M, symm =  TRUE, col = viridis(256), hclustfun = clust_col)

```


```{r}

m2 <- similitud
#m2[,-1][lower.tri(m2[,-1],diag = TRUE)] <- NA

sim_table <- m2 %>% 
  gather(SITC_par,value = similarity,2:ncol(.)) %>%
  filter(!is.na(similarity)) %>% 
  mutate(distance = 1/similarity)

```

```{r}

ggplot(sim_table,aes(SITC, reorder(SITC_par, desc(SITC_par)), fill = similarity))+
  geom_tile()+
  # scale_x_discrete(breaks = names[seq(1,length(names),100)])+
  # scale_y_discrete(breaks = names[seq(1,length(names),100)])+
  # theme_tufte()+
  theme_void()+
  labs(x='',y='',fill="Proximity")+
  scale_fill_viridis()+
  theme(legend.position = 'bottom')

ggsave('results/heatmap_prox_sitcOrd_lp.png',height = 6,width = 6,dpi = 300)

```


```{r}
M_thr <- M
# M_thr[M_thr<0.5] <- 0

DM_thr <- 1/M_thr
DM_thr[is.infinite(DM_thr)] <- 99999999999
d <- as.dist(DM_thr)

cluster_h <- hclust( d, method = "ward.D" )

order <- cluster_h$order

ordered_names <- colnames(DM_thr)[order]

sim_table %>% 
  mutate(SITC = factor(SITC, levels = ordered_names),
         SITC_par = factor(SITC_par, levels = rev(ordered_names))
         # similarity = case_when(similarity<0.5 ~0,
         #                        TRUE ~similarity)
         ) %>% 
ggplot(.,aes(SITC, SITC_par, fill = similarity))+
  geom_tile()+
  # scale_x_discrete(breaks = ordered_names[seq(1,length(ordered_names),100)])+
  # scale_y_discrete(breaks = ordered_names[seq(1,length(ordered_names),100)])+
  theme_void()+
  labs(x='',y='',fill="Proximity")+
  scale_fill_viridis()+
  theme(legend.position = 'bottom')

ggsave('results/heatmap_prox_ClustOrd_lp.png',height = 6,width = 6,dpi = 300)

```


```{r}
m3 <- m2
m3[,-1][lower.tri(m3[,-1],diag = TRUE)] <- NA


sim_table <- m3 %>% 
  gather(SITC_par,value = similarity,2:ncol(.)) %>%
  filter(!is.na(similarity))
```

```{r}

sim_table %>% 
  arrange(-similarity) %>% 
  top_n(10, similarity) %>%
  left_join(clasificacion %>% select(SITC= medioide, description=sitc_product_name_short_en)) %>% 
  left_join(clasificacion %>% select(SITC_par= medioide, description_par=sitc_product_name_short_en)) %>% 
  mutate(description = paste0(substr(description,1,22),'...'),
         description_par = paste0(substr(description_par,1,22),'...')) %>% 
  xtable::xtable(., caption='Productos más similares', label='table:similarity') %>% 
  xtable::print.xtable(.,include.rownames=FALSE)

```

```{r}
mean_sim_table %>% 
  arrange(-similarity) %>%
  top_n(25, similarity) %>% 
  filter(row_number() %% 2 == 0) %>% 
  left_join(clasificacion %>% select(SITC=medioide,SITC_desc=Description)) %>% 
  left_join(clasificacion %>% select(SITC_par=medioide,SITC_par_desc=Description)) %>% 
  select(similarity,SITC, SITC_desc,SITC_par,SITC_par_desc) %>% 
  mutate(SITC_desc = tolower(SITC_desc),
         SITC_par_desc = tolower(SITC_par_desc),
         SITC_desc = str_remove(SITC_desc,",x-knit"),
         SITC_par_desc = str_remove(SITC_par_desc,",x-knit"),
         SITC_desc = str_remove(SITC_desc,".xknit"),
         SITC_par_desc = str_remove(SITC_par_desc,".xknit"),
         SITC_desc = str_remove(SITC_desc,".knit"),
         SITC_par_desc = str_remove(SITC_par_desc,".knit")) %>% 
  xtable::xtable(., caption='Productos más similares', label='table:similarity') %>% 
  xtable::print.xtable(.,include.rownames=FALSE)


```

